News Recommenders: Real-Time, Real-Life Experiences

Recommender systems have become an essential part of our daily lives. In this
work we present our early experiences when it comes to a real-world news recommendation
task in the guise of the 2014 NewsREEL challenge [6]. NewsREEL
participants respond to recommendation requests with their suggestions and,
crucially, received live-user feedback in the form of click-through data, providing
an opportunity for a large-scale evaluation of recommender systems in the wild.
In contrast to other domains (books, movies, etc.), news has a greater item
churn [2,4] and users are much more sensitive to article recency [4]. Furthermore,
user profiles are typically constrained to the current session [11] or at best defined
by browser cookies [7]. Moreover, it is atypical for users to evaluate articles explicitly
and feedback is commonly collected implicitly by observing user behaviour
[8]. Finally, there is a low consumption cost associated with reading a news article
which can result in a larger than normal diversity of the consumed items [10].
Within the context of the NewsREEL challenge [5] (formerly, The News Recommender
System Challenge (NRS’13)), Said et al. [9] study the performance
of similarity- and recency-based algorithms. They find that both types perform
slightly better when recommending in general news sites than in more topic
focussed sites. Others [4,5] recommend representing a news article using a combination
of metadata and contextual features; see [7] for a full list of such features
available for the NewsREEL challenge.

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